Automated image analysis for magnetic resonance imaging

ABSTRACT

A magnetic resonance imaging (MRI) system, comprising: a magnetic resonance imaging scanner configured to generate a plurality of signals for forming at least one magnetic resonance image of a soft tissue region from a subject under observation, wherein the at least one magnetic resonance image provides at least one integrating feature to facilitate automatic segmentation; a signal processing system in communication with the magnetic resonance imaging scanner to receive the plurality of signals; and a data storage unit in communication with the signal processing system, wherein the data storage unit contains at least one template corresponding to the soft tissue region, wherein the signal processing system is adapted to process the plurality of signals received from the magnetic resonance imaging scanner to automatically perform segmentation for the soft tissue region of the subject under observation by utilizing the at least one template and the at least one integrating feature.

CROSS-REFERENCE OF RELATED APPLICATION

This application is a Continuation of U.S. patent application Ser. No.12/747,816, filed Jun. 11, 2010, which is a 371 of InternationalApplication PCT/US2009/032866, filed Feb. 2, 2009, which claims priorityto U.S. Provisional Application No. 61/063,025, filed Jan. 31, 2008, theentire contents all of which are hereby incorporated by reference.

BACKGROUND

1. Field of Invention

The current invention relates to automated identification of soft tissuesubstructures in a soft tissue region of a human or animal subject in anon-invasive manner.

2. Discussion of Related Art

Although advancements in imaging hardware and software havesubstantially improved imaging throughput and image quality, thereremains a bottleneck in radiological diagnosis. Currently, radiologicaldiagnosis is mostly based on subjective visual inspection and judgment.However, manual reading is laborious and time-consuming. Furthermore,quite often, the amount of abnormality is not large compared to thenormal range of subject variability. Quantitative analysis maysignificantly benefit current radiological diagnosis by improving ourability to detect and characterize abnormalities in a robust andreproducible manner. The current lack of quantitative analysis inclinical routines stems from, at least partly, difficulties in analyzingtissues based on magnetic resonance (MR) images. In neurologicaldiagnosis, for example, high quality segmentation of the brain boundaryrequires a considerable amount of manual labor, which typically takes2-4 hours for segmenting individual brains. Further segmentation of thebrain into tissue classes takes even more time by manual labor. This mayhinder delivery of quality service to our aging society with risingincidences of, for example, Alzheimer's disease. Existing automatedprograms for segmentation only provide approximate segmentation resultsthat are inadequate for wide adoption in clinical routines. Thus, thereis a need for an improved automatic segmentation of soft tissuestructures of a person or an animal.

SUMMARY

Some embodiments of the current invention provide a magnetic resonanceimaging (MRI) system, comprising:, a magnetic resonance imaging scannerconfigured to generate a plurality of signals for forming at least onemagnetic resonance image of a soft tissue region from a subject underobservation, wherein the at least one magnetic resonance image providesat least one integrating feature to facilitate automatic segmentation; asignal processing system in communication with the magnetic resonanceimaging scanner to receive the plurality of signals; and a data storageunit in communication with the signal processing system, wherein thedata storage unit contains at least one template corresponding to thesoft tissue region, wherein the signal processing system is adapted toprocess the plurality of signals received from the magnetic resonanceimaging scanner to automatically perform segmentation for the softtissue region of the subject under observation by utilizing the at leastone template and the at least one integrating feature,

Some embodiments of the current invention provide a workstation,comprising: preprocessing engine to receive and preprocess magneticresonance signals, from a magnetic resonance scanner, wherein themagnetic resonance signals form at least one magnetic resonance imageshowing a soft tissue region from at least one subject under observationin the magnetic resonce scanner; and a normalizing engine toautomatically segment the soft tissue region by normalizing the formedat least one magnetic resonance image based on at least one templatecorresponding to the soft tissue region, wherein the at least onemagnetic resonance image provides at least one integrating feature tofacilitate automatic segmentation.

Some embodiments of the current invention provide a method comprising:receiving magnetic resonance signals for forming at least one magneticresonance image showing a soft tissue region from a subject, wherein theat least one magnetic resonance image provides at least one integratingfeature to facilitate automatic segmentation; preprocessing saidmagnetic resonance signals to generate at least one magnetic resonanceimage; receiving at least one template corresponding to the soft tissueregion; and normalizing the generated at least one magnetic resonanceimage based on the at least one template to generate a segmented view ofthe soft tissue region.

Some embodiments of the current invention provide a computer readablemedium, when executed by a computer, causes the computer to implementthe method above.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objectives and advantages will become apparent from aconsideration of the description, drawings, and examples.

FIG. 1 is a schematic illustration of a magnetic resonance imaging (MRI)system according to an embodiment of the current invention.

FIG. 2 is a flow chart to facilitate the explanation of an embodiment ofthe current invention.

FIG. 3A shows a magnetic resonance image with T₂ weighting.

FIG. 3B shows a magnetic resonance image with T₁ weighting.

FIG. 3C shows a magnetic resonance image with skull suppression.

FIG. 4A shows a 2-dimensional (2-D) view of a brain template exampleaccording to an embodiment of the current invention.

FIG. 4B shows a 3-dimensional (3-D) view of a brain template exampleaccording to an embodiment of the current invention

FIG. 5A shows an example of a probabilistic map according to anembodiment of the current invention for peripheral white matter (PWM)with greater than 60% probability.

FIG. 5B shows an example of a probabilistic map according to anembodiment of the current invention for peripheral white matter (PWM)with greater than 70% probability.

FIG. 5C shows an example of a probabilistic map according to anembodiment of the current invention for peripheral white matter (PWM)with greater than 80% probability.

FIG. 5D shows an example of a probabilistic map according to anembodiment of the current invention for peripheral white matter (PWM)With greater than 90% probability.

FIG. 6A shows examples of registering the brain images from anAlzheimer's patient to a template according to an embodiment of thecurrent invention.

FIG. 6B shows an example of registering a brain image from anAlzheimer's patient to a template according to an embodiment of thecurrent invention.

FIG. 6C shows an example of registering a template to a brain image froman Alzheimer's patient according to an embodiment of the currentinvention.

FIG. 6D shows examples of automated brain segmentation according to anembodiment of the current invention.

FIG. 7 is a schematic illustration of a workstation according anembodiment of the current invention.

DETAILED DESCRIPTION

Some embodiments of the current invention are discussed in detail below.In describing embodiments, specific terminology is employed for the sakeof clarity. However, the invention is not intended to be limited to thespecific terminology so selected. A person skilled in the relevant artwill recognize that other equivalent components can be employed andother methods developed without departing from the broad concepts of thecurrent invention. All references cited herein are incorporated byreference as if each had been individually incorporated.

FIG. 1 is a schematic illustration of a magnetic resonance imaging (MRI)system 100 according to an embodiment of the current invention

The MRI system 100 includes a magnetic resonance scanner 101, a datastorage unit 108, and a signal processing unit 109. Magnetic resonancescanner 101 has a main magnet 105 providing a substantially uniform mainmagnetic field B₀ for a subject 192 under observation on scanner bed103, a gradient system 106 providing a perturbation of the main magneticfield B₀ to encode spatial information of the constituent watermolecules of subject 102 under observation, and a radio-frequency (RF)coil system 107 to transmit electromagnetic waves and to receivemagnetic resonance signals from subject 102. Magnetic resonance scanner101 is configured to generate magnetic resonance signals that facilitatesubsequent analysis including segmentation.

Data storage unit 108 stores template data corresponding to a softtissue region of subject 102 under observation. The soft tissue regionmay be, for example, a brain, a heart, a muscle, etc. The template datacomprises at least one template, A template of a soft tissue region maybe derived from, for example, a plurality of magnetic resonance imagesfrom a subpopulation with similar characteristics to that of subject102. For example, template data can come from the same age group assubject 102 in some applications. This is because each age range mayhave different tissue shapes and contrasts. For example, the human brainundergoes considerable shape and contrast changes during the first 24months of age. During this period, templates should be created with asmall range of ages (e.g., 0-1 month. 1-2 months, 2-4 months, etc.). Onthe other hand, however, the brain undergoes aging processes whichbecome apparent approximately after age 60, Therefore additionaltemplates may be required for an older subpopulation. A template mayinclude magnetic resonance images from at least one subject that isdifferent from subject 102 under observation. A template may includemagnetic resonance images from a previous scan of subject 102 underobservation. For example, one template may be derived from T₁-weightedimages suitable for the cortex and deep gray matter structures, a secondtemplate may be derived from T₂-weighted images having higher contrastsfor ventricles, and a third template may be derived from diffusiontensor images in which intra-white matter structures are bestdelineated. The term template data is thus defined and shall beunderstood consistently throughout the confines of this paper.

Data storage unit 108 may be, for example, a hard disk drive, a networkarea storage (NAS) device, a redundant array of independent disks(RAID), a flash drive, an optical disk, a magnetic tape, amagneto-optical disk, etc. However, the data storage unit 108 is notlimited to these particular examples. It can include other existing orfuture developed data storage devices without departing from the scopeof the current invention.

A signal processing system 109 is in communication with magneticresonance scanner 101 to receive magnetic resonance signals for formingmagnetic resonance images of subject 102. Signal processing system 109may be partially or totally incorporated within a structure housingmagnetic resonance scanner 101. Signal processing system 109 may be atleast partially incorporated in a workstation that is structurallyseparate from and in communication with magnetic resonance scanner 101.Signal processing system 109 may be incorporated in a workstation thatis structurally separate from and in communication with magneticresonance scanner 101. Magnetic resonance signals received by signalprocessing system 109 may be associated with a magnetic resonancecontrast parameter, such as, for example, a relaxation time T₁ arelaxation time T₂, an apparent diffusion coefficient, a propertyassociated with the blood oxygenation level dependent (BOLD) effect, aproperty associated with the diffusion tensor, etc.

Signal processing system 109 is in communication with data storage unit108. By utilizing the template data stored on data storage unit 108;signal processing system 109 is capable of processing the magneticresonance signals received from magnetic resonance scanner 101 toautomatically segment a soft tissue region Within the magnetic resonanceimages of subject 102. The results may be displayed on a viewing station110 or a console station 111. The segmentation results may be used forfurther image analysis and disease diagnosis.

FIG. 2 is a flow chart to help describe an embodiment of the currentinvention.

In block 201, magnetic resonance signals that may include an integratingfeature to facilitate subsequent analysis including segmentation areacquired from magnetic resonance scanner 101. The integrating featuresmay be provide by techniques that include but are not limited tointensity homogeneity corrections, multi-contrast, skull-suppressioncontrast, and quantitative parameterization.

Intensity homogeneity corrections may ensure that the intensity profileon the imaged object is homogeneous, which facilitate the efficiency andaccuracy of subsequent image analysis. Examples of intensity homogeneitycorrections may include but are not limited to: for example, propershimming, judicious choices of RF coils with more uniform sensitivityprofiles, use of reference phantoms, etc.

Multi-contrast may include contrasts associated with T₁, T₂, T₂*,proton-density, apparent diffusion, perfusion, diffusion tensor, etc.The contrasts may be realized by including inversion-recovery pre-pulses(also known as preparation pulses in the literature). Each contrastdelineates brain structures differently. For example, T₁ may providehigh differentiation between gray and white matter and T₂ may define theventricles better. It is noted that integration with detailed imagingparameters, such as echo time, repetition time, inversion recovery time,field of view, and imaging matrix, etc., may further ensure standardizedimage quality of analyzed images.

Skull-suppression contrast may include approaches to suppress magneticresonance signals from adjacent tissues such as dura, fat, and skin,etc. In general, signal intensity associated with a magnetic resonanceimage is affected by various physical and chemical properties of watermolecules such as T₁, T₂, T₂*, proton density, perfusion, and diffusion.Magnetic resonance signal intensity from a spin-echo sequence may bedescribed as:

S=PD·(1−e ^(−TR1T))·e ^(−bD)   (1),

where PD is proton density signal, TR is repetition time, TB is echotime, b is a factor due to a pair of diffusion encoding gradientsassociated with a spin echo imaging sequence, D is the diffusioncoefficient. Example approaches may, for example, include judiciouschoice of TRs and TEs to minimize magnetic resonance signals fromnon-brain tissues. Example approaches may also utilize the shift inresonance frequency of signals from fat and non-fat tissues tosuppression fat signals.

Quantitative parameterization may include parameterization of signalsassociated with magnetic resonance images. For example, by acquiringmore than one magnetic resonance image with different imagingconditions, parameters such as T₁, T₂, and diffusion coefficient D maybe quantified by using magnetic resonance sequences. Diffusioncoefficient D may be quantified as, for example, an apparent diffusioncoefficient (ADC), a diffusion tensor (DT), a quantity associated withthe diffusion tensor matrix, etc. In general, parameterization may easecomparison between two images from different subjects or scanners.

In block 202, magnetic resonance signals acquired are pre-processed togenerate magnetic resonance image data 203.

For example, preliminary brain segmentation may be performed byintensity thresholding or region growing using an acquiredskull-suppressed magnetic resonance image to roughly define, forexample, a brain.

For example, template-based refinement may then be performed by morphinga binary brain, template to the preliminarily segmented brain in orderto remove, for example, various non-brain tissues remaining in thepreliminarily segmented brain image.

For example, the refined brain segmentation results may be used asmasking to automatically remove non-brain tissue in co-registeredmagnetic resonance images acquired from the same subject 102.

For example, the above segmented image may be used as a template tocorrect distortions contained in some magnetic resonance images. Thedistortions may be due to B₀ inhomogeneity resulting from, for example,sharp susceptibility differences at interfaces of tissue and air. Forexample, a segmented skull-suppressed image may be used as a template towhich images containing distortion may be non-linearly and automaticallywarped using such algorithms as Large Deformation DiffoMorphism Metric(LDDMM).

MRI-based template data 204 may reside on data storage unit 108.MRI-based template data 204 may be created by using magnetic resonanceimages from a single subject or probability-based images from manysubjects, encoding the spatial probability of a soft tissue. The imagesmay show segmented tissues of interest based on various signal contrastsuch as, for example, T₁, T₂, T₂*, proton density, diffusion, perfusion,blood oxygenation level dependent (BOLD), etc,

In block 205, MR image data 203 is normalized based on MRI-basedtemplate data 204 to generate segmented MR image data 206. Thenormalization may include non-linear warping of template data 204 toimage data 203 using, for example, large deformation diffomorphic metric(LDDMM). The normalization may also match template data 204 with imagedata 203 based on signal contrast (e.g., T₁ to T₁, T₂ to T₂, anddiffusion to diffusion, etc.) in a multi-channel manner, Segmented MRimage data 206 may be used in a number of applications. For example, tocalculate the volume of a soft tissue of interest in segmented MR imagedata 206, one may simply count the number of pixels within the morphedsegments.

In block 207, automated analysis based on segmented MR image data 206may be performed. Automated analysis may analyze the transformationmatrix to retrieve quantitative data about brain shapes. For example,Jacobian maps associated with the morphing of template data 204 to MRimage data 206 may be calculated to reveal local tissue shrinkage andenlargement compared to the template. For example, the transformationmatrix associated with morphing an image in MR image data 206 totemplate data 204 may be applied to other images showing differentcontrast, and quantitative parameterized maps such as T₁, T₂, diffusioncoefficient D, etc. The application may allow further and quantitativeanalyses to detect signal abnormalities.

In block 208, results from block 207 may be compared with knowledgedatabase 209 to generate diagnosis result 210. Knowledge database 209may be include, for example, volumes of segmented tissues, morphologicalmaps, and photometric maps from a normal population of similar agerange. Averages and standard deviations of quantitative measures derivedfrom magnetic resonance data of the normal population may be included aswell. Knowledge database 209 may also include volumes of segmentedtissues, morphological maps, and photometric maps from a clinicalpopulation of similar age range diagnosed as patients. The termknowledge data base is thus defined and shall be understood consistentlywithin the confines of this paper. The comparison of results from block207 with knowledge database 209 may be implemented using correlationtechniques such as, for example, the t-test, p-test, StatisticalAnalysis Software (SAS), etc. If diagnosis result 210 is positive, thenresults from block 207 being compared may be added to knowledge database209 as part of the data corresponding to the clinical population. Thequantitative results from block 207 and data from the normal populationof similar subject group contained in knowledge database 209 may also bedisplayed, for example, on console station 110 or workstation 111,.for auser, for example, a clinician, to make a diagnosis.

Some embodiments of the current invention may include a computerreadable medium, which when executed by a computer, implements the flowchart in FIG. 2. A computer readable medium may refer to any storagedevice used for storing data accessible by a computer. Examples ofcomputer readable mediums may include but are not limited to: a magnetichard disk; a floppy disk; an optical disk, such as a CD-ROM and a DVD; amagnetic tape; and a memory chip.

FIG. 3A shows an example magnetic resonance image with T₂ weighting.Pixels with brighter intensities tend to associate with longer T₂s.

FIG. 3B shows an example magnetic resonance image with T₁ weighting.Pixels with brighter intensities tend to associate with shorter T₁s.

FIG. 3C shows an example magnetic resonance image with skullsuppression. The skull suppression may be achieved, inter alia, via fatsuppression MR sequences to null MR signals from subcutaneous fat.

Images of similar contrasts to FIGS. 3A-3C may be acquired andpre-processed according to an embodiment of the current invention.

FIG. 4A shows a 2-dimensional (2-D) view of a brain template exampleaccording to an embodiment of the current invention. Based onT₁-weighted and diffusion tensor imaging (DTI) contrasts, the brain issegmented into 176 anatomical areas.

FIG. 4B shows a 3-dimensional (3-D) view of the same brain templateexample according to an embodiment of the current invention. Thetemplate is visualized as three anatomical regions, namely, the GrayMatter (GM), the Peripheral White Matter (PWM), and Core White Matter(CWM).

In the example above, the brain is divided into 176 anatomical areasbased on T₁-weighted and diffusion tensor imaging (DTI) contrasts. Amongthem, there are 80 GM areas, 46 PWM areas, and 50 CWM areas. Inparticular, the segmentation of gray matter structures was based oninformation from T₁-weighted images while the segmentation of whitematter structures was partially based on information from diffusiontensor imaging (DTI). Specifically, the gray matter, including thecortex and the basal ganglia, was segmented using T₁-weightedMagnetization-Prepared Rapid Gradient Echo (MPRAGE) images. The CWMareas were defined using DTI images based on two criteria, First, theseareas were well characterized in past anatomical studies. Second, theirsizes and locations were well-defined and highly reproducible amongnormal subjects. The white matter that fills the space between thecortex of the gray matter and the core white matter is the PeripheralWhite Matter (PWM). PWM areas are further subdivided based on thecortical segmentation.

In the example above, the brain template, as one embodiment of thecurrent invention, may be referred to as a “Level 1” template in whichthe brain from a single subject is divided into a number of anatomicalunits. The data for the brain template in the example above was obtainedfrom a 32-year-old healthy female subject without previous neurologicaldisorders. A 1.5T MR unit (Gyroscan NT, Philips Medical Systems) wasused.

DTI data were acquired using single-shot echo-planar imaging sequenceswith sensitivity encoding. (SENSE) and a parallel imaging factor of 2.5(Pruessmann et al., 1999 SENSE: sensitivity encoding for fast MRI, MagnReson Med 42, 952-962). The imaging matrix was 112×112 in k-space, witha field of view of 246×246 mm and an accompanying nominal resolution of2.2 mm. The image matrix was zero-filled to 256×256 pixels in k-spaceprior to reconstruction. Transverse image slices were acquired at 2.2 mmslice thickness and were parallel to the anterior commissure-posteriorcommissure line (AC-PC). A total of 60 image slices covered the entirehemisphere and brainstem with no gaps. Diffusion weighting gradientswere applied along 30 independent orientations (Jones et al., 1999) withan accompanying b-value of 700 mm²/s. Five additional images withminimal diffusion weighting (b=33 mm²/sec) were also acquired. Toenhance the signal-to-noise ratio, imaging was repeated six times. Foranatomical guidance in each slice location, double-echo (echo time of 40and 100 ms) Fast Spin-Echo (FSE) images and one Magnetization-PreparedRapid Gradient Echo (MPRAGE) image with 1 mm image resolution wererecorded and co-registered,

The raw diffusion-weighted images (Mils) were first co-registered to oneof the images with the minimal diffusion weighting and was corrected forsubject motion and eddy current distortion using a 12-mode affinetransformation of Automated Image Registration (AIR) (Woods et. at,1998, Automated image registration: I. General methods and intrasubject,intramodality validation. J Comput Assist Tomogr 22, 139-152).B₀-susceptibility distortion was corrected by non-linear warping usingthe FSE (echo time of 100 ms) image with the minimal diffusion weighting(Huang at al., 2005. Magn. Reson. Imaging, 26, 1294-1302). For thenon-linear warping, a Large Deformation Diffeomorphic Metric Mapping(LDDMM) was used (Miller et al., 1997, Stall Methods med Res, 6,267-299). The warping was applied to all raw DWIs, which were thenre-sliced to 1 mm isotropic resolution (246×246×121 matrix). The sixelements of the diffusion tensor were calculated for each pixel withmultivariate linear fitting using DtiStudio (Jiang et al., 2006, ComputMethods Programs Biomed, 81, 106416). After diagonalization, threeeigenvalues and eigenvectors were obtained. For the anisotropy map,fractional anisotropy (FA) was used (Pierpaoli and. Basser, 1996. Towarda quantitative assessment of diffusion anisotropy. Magn.Reson.Med. 36,893-906.).

The Level 1 template above, was created based on the InternationalConsortium of Brain Mapping (ICBM-152) template coordinates. A JohnsHopkins University(JHU)-DTI template was transformed to the ICBM-152template using a simple affine transformation. The native MPRAGE imagewas also transformed to the ICBM-152. The transformation matrix was thenapplied to the calculated diffusion tensor field of the JHU-DTI atlasusing the method described by (Alexander at al., 2001, IEEE Trans Medimaging 20, 1131-1139) and (Xu et al., 2002, Magn Reson Med, 50,175-182).

While the Level 1 template above contains anatomical information ingreat detail, it may contain subject-specific anatomical information(such as cortical folding pattern), which may not be distinguished fromstructures that are reproducible among subjects. A “Level 2” template,however, incorporates information about the probability of eachanatomical structure within the template over a population (Mori et al.,2008, Neuroimage, 40, 570-582, Mori at al. PCT/US/2009/000011, AutomaticFiber Tracking of Human Brain White Matter Using Diffusion TensorImaging). The contents of this patent application is incorporated hereinby reference. The notion of probability (or reproducibility) can beimportant for the PWM and the cortex of gray matter. To generate theprobabilistic maps of the PWM, the white matter of 30 normal subjectswere segmented and registered to the Level 1 template using LDDMM. Bylumping the 30 registered segmentation maps, the spatial probability ofwhite matter can be calculated at each Montreal Neurological Institute(MNI) coordinate. The probabilistic map, obtained from multiplesubjects, may be referred to as a Level 2 template.

FIG. 5A-5D show examples of probabilistic maps, according to anembodiment of the current invention, for peripheral white matter (PWM)with greater than 60%, 70%, 80%, and 90% probabilities.

Using the Level 2 template above, the basal ganglia, the CWM, and thePWM can be defined automatically in a given subject. Once the templateis successfully applied to a subject, it is straightforward to measurequantities such as volumes arid MR contrast parameters (T₁, T₂, T₂*,ADC, fractional anisotropy, eigenvalues, etc.) for a given segmentedregion or area. In the Level 2 template, quantitative measures such asthe average and standard deviation values may be defined in a segmentedregion or area, with which the patient values can be compared. It may bepossible to segment and analyze each and every substructure of, forexample, the cortex, with satisfactory accuracy and precision. However,embodiments of the current invention, with only the cortex areassegmented, can still produce sufficient anatomical information about thecortex, such as proportions and volumes of the cortical areas.

Once the template boundary is defined based on apopulation-probabilistic map (e.g., 90% white matter), it may be mucheasier to identify corresponding common white matter areas acrosssubjects, compared to the Level I template based on a single subject.

FIG. 6A shows examples of normalizing the brain images from anAlzheimer's patient based on a template according to an embodiment ofthe current invention. The normalization maps the patient's image datato our template data. Once the normalization of the subject brain imagesto the template is completed, the pre-segmented substructures, forexample, the 176 segmented brain areas defined within the templatesabove, can be automatically transferred and superimposed on the imagedata of a given patient. Since the segmented brain areas are now fittedto the data from the subject, quantities of physiological interestwithin a segmented area of the subject may be measured. The quantitiesmay include but are not limited to: volumes, shapes, and various MRcontrast parameters, such as T₁, T₂, diffusion coefficient D, etc.

A transformation algorithm, called Large Deformation DiffeomorphicMetric Mapping (LDDMM) (Miller et al., 1993, Proc Natl Acad Sci, 90,1194-11948; Joshi et al., 1995, Geometric methods in Applied Imaging,San Diego, Calif.; Granander and Miller, 1996, Statistical computing andgraphics newsletter 7, 3-8), may be used during the normalization. Therecan be several important technically attractive features of LDDMM.First, LDDMM is highly non-linear and can match the shapes of twobrains, as demonstrated in FIG. 6A. It can even transform a brain withsevere atrophy. Second, LDDMM can achieve topology preservation.Topology preservation may be an very important feature when applying amorphing algorithm to a biological entity. For example, when morphingone face to another, if topology is not preserved, non-biologicalresults (e.g., two eyes become three eyes) can occur. Third, thetransformation can be reciprocal. it is noted that other transformationalgorithm that can generate image transformation and preserve tissuetopology can be used instead of LDDMM. In some cases, e.g. only subtlechanges in soft tissue region are expected, the requirement of topologypreserving can be waived.

Normalizing an image based on a template may be performed by registeringthe image to the template or by registering the template to the image.FIG. 6B shows an example of registering a brain image from anAlzheimer's patient to a template according to an embodiment of thecurrent invention using LDDMM. The patient image is registered to thetemplate and the boundaries of segmented structures boundaries in thetemplate are superimposed on the patient image.

FIG. 6C shows an example of registering a template to a brain image froman Alzheimer's patient, according to an embodiment of the currentinvention using LDDMM. The brain segmentation data in the template istransformed to the patient image for automated segmentation. Thisapproach can avoid deformation and interpolation of the patient image.Volume measurement of each segmented area in the patient image can alsobe more straightforward.

In FIG. 6B, an example of registering a patient image to a template isshown. However, for the automated quantification of individual patients,the registration of a template to a patient image, as shown in FIG. 6C,may provide more intuitive results. By using LDDMM, the tworegistrations may be reciprocal.

The LDDMM algorithm computes a transformation, φ:Ω→Ω, between two imagespaces, where Ω⊂R^(e) is the 3D cube by which the data is defined. Thetransformation computed by LDDMM is generated as the end point, φ=φ₁, ofthe flow of smooth time-dependent vector fields, v_(i), with theordinary differential equation, {circumflex over (φ)}_(i)=v_(i), t ∈[0,1]. where φ₀(x)_32 x,x ∈ Ω.

Smoothness of the vector fields, v_(i), may ensure that the computedtransformation is a diffeomorphism. This smoothness condition may beenforced with a regularization term based on constraints induced by asuitable Sobolev norm, denoted as ∥v∥_(v). The generic inexact matchingproblem may be formulated as the following equation:

$\begin{matrix}{\mspace{79mu} {{\hat{\phi} = {{\text{?}{\int_{0}^{1}{{v_{t}}_{V}^{2}\ {t}}}} + {D(\phi)}}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (1)\end{matrix}$

where D(φ) is a matching term between the transformed source and targetdata. Depending on the input data (landmarks, scalar valued images,vector-valued images or tensor-valued images), different versions ofLDDMM can be defined with different matching terms.

To determine the best correspondence between one or several scalarobservations on Ω, denoted I₀₁, . . . , I_(0C), for the source and I₁₁,. . . , O_(1C) for the target, one can choose, for example

${D(\phi)} = {\sum\limits_{c = 1}^{C}\; {\frac{1}{\sigma_{c}^{2}}{\int_{\Omega}^{\;}{\left( {I_{0c} - I_{1c}} \right)^{2}\ {{x}.}}}}}$

An example minimization algorithm, (Beg Et Al., 2005, InternationalJournal of Computer Vision, 61, 139-157), implements a gradient descentprocedure with respect to the time-dependent vector field, v_(t) For thesource image, one can test. LDDMM using five channels of data,including, for example, MPRAGE, proton density (TE=40 ms), T2-weighted(TB=100 ms), ADC maps, and fractional anisotropy maps.

The accuracy of automated quantitative analysis may depend on thequality of image registration. A high quality registration may manifestas the shapes of the two images being matched as closely as possible, Toachieve the closest matching in shape, non-linear image transformationmay be used. Because the shapes of two images are often very different,a “large deformation” transformation may be used. An aspect of the LDDMMis that it allows large deformation while maintaining the topology ofthe object. For example, one topologically spherical object (e.g., thethalamus) may remain as one object. This simple condition is not alwayspresent for various types of non-linear transformations. For example,one part of the thalamus may be detached from the other after anon-linear transformation, resulting in two separate (one large and onesmall) thalamic objects. The inability to maintain topology may occurmore often as the elasticity of non-linear warping increases, which maybe needed by conventional non-linear transformation algorithms toachieve excellent shape matching.

FIG. 6D shows examples of automated brain segmentation according to anembodiment of the current invention. Results from three subjects areshown using the LDDMM registration algorithm and Level 2 template with90% probabilistic maps. Thus, using the Level 2 template, one may definethe basal ganglia, the CWM, and the PWM automatically. Once the templateis successfully applied to a given subject, it is straightforward tomeasure quantities such as volumes and MR contrast parameters (forexample, T₁, T₂, T₁*, ADC, fraction anisotropy, eigenvalues of thediffusion tensor, etc.) for a segmented area in an image of the subject,

FIG. 7 is a schematic illustration of a workstation according anembodiment of the current invention. Workstation 700 may comprisepreprocessing engine 701 and normalizing engine 702.

Preprocessing engine 701 may, for example, perform preliminary automatedbrain segmentation on MR signals 705 by intensity thresholding or regiongrowing using an acquired skull-suppressed magnetic resonance image toroughly define, for example, a brain. Preprocessing engine 701 may, forexample, perform template-based refinement of automated brainsegmentation by morphing a binary brain template to the roughlysegmented brain in order to remove, for example, various non-braintissues remaining in the roughly segmented brain. image. Preprocessingengine 701 may, for example, use the refined brain segmentation resultsas masking to automatically remove non-brain tissue in co-registeredmagnetic resonance images acquired alike. Preprocessing engine 701 may,for example, use the above segmented image as a template to correctdistortions contained in some magnetic resonance images. The distortionsmay be due to Bo inhomogeneity resulting from, for example, sharpsusceptibility differences at interfaces of tissue and air.Preprocessing engine 701 may, for example, further use the segmentedskull-suppressed image as a template to which images containingdistortion may be non-linearly and automatically warped using algorithmssuch as LDDMM.

Normalizing engine 702 may normalize the results from preprocessingengine 701 based on MRI-based template data 204 to generate segmented MRimage data 206. Normalizing engine 702 may perform non-linear warpingusing, for example, large deformation diffomorphic metric (LDDMM).Normalizing engine 702 may also match template data 204 with the resultsfrom preprocessing engine 701 based on signal contrast (e.g., T₁ to T₁,T₂ to T₂, and diffusion to diffusion, etc.) in a multi-channel manner.

Workstation 700 may further comprise analyzing engine 703 that analyzesthe transformation matrix associated with morphing to retrievequantitative data about brain shapes. For example, analyzing engine 703may analyze Jacobian maps associated with the morphing of template data704 to MR image data 206 to reveal local tissue shrinkage andenlargement compared to the template. For example, analyzing engine 703may apply the transformation. matrix, associated with morphing an imagein the results from preprocessing engine 701 to template data 204, toother images showing different contrast as well as quantitativeparameterized maps of, for example, T₁, T₂, diffusion, etc. Thus, theapplication of the transformation-matrix may allow further andquantitative analyses to detect signal abnormalities.

Workstation 700 may further comprise diagnosing engine 704 that comparesthe results from analyzing engine 703 with knowledge database 209 togenerate diagnosis result 210. Diagnosing engine 704 may correlate theresults from analyzing engine 703 with knowledge database 209 byincorporating such statistical tools as the t-test, p-test, SAS, etc. Ifdiagnosis result 210 is positive, then diagnosing engine 304 may add thediagnosis result 210 being generated to knowledge database 209. Thequantitative results in diagnosis results 210 and quantitative data fromknowledge database 209 may also be displayed, for example, on consolestation 110 or workstation 111, for a user, for example, a clinician, tomake a diagnostic decision,

Workstations 700 may be a computer with at least one central processingunit (CPU) and a plurality of memories. Workstations 700 may also be adedicated processing machine with such devices as, for example, a fieldprogrammable gated array (FPGA), a digital signal processing (DSP) chip,a graphic processing unit (GPU), an application specific integratedcircuit (ASIC), etc.

Preprocessing engine 701, normalizing engine 702, analyzing engine 703,and diagnosing engine 704 may be implemented by a computer with at leastone central processing unit (CPU) and a plurality of memories,Preprocessing engine 701, normalizing engine 702, analyzing engine 703,and diagnosing engine 704 may be implemented as, for example, a fieldprogrammable gated array (FPGA), a digital signal processing (DSP) chip, a graphic processing unit (GPU), an application specific integratedcircuit (ASIC), etc.

In describing embodiments of the invention, specific terminology isemployed for the sake of clarity. However, the invention is not intendedto be limited to the specific terminology so selected. Theabove-described embodiments of the invention may be modified or varied,without departing from the invention, as appreciated by those skilled inthe art in light of the above teachings. It is therefore to beunderstood that, within the scope of the claims and their equivalents,the invention may be practiced otherwise than as specifically described

1. A magnetic resonance imaging (MRI) system, comprising: a magneticresonance imaging scanner configured to generate a plurality of signalsfor forming at least one magnetic resonance image of a soft tissueregion from a subject under observation, wherein said at least onemagnetic resonance image provides at least one integrating feature tofacilitate automatic segmentation; a signal processing system incommunication with said magnetic resonance imaging scanner to receivesaid plurality of signals; and a data storage unit in communication withsaid signal processing system, wherein said data storage unit containsat least one template corresponding to said soft tissue region, whereinsaid signal processing system is adapted to process said plurality ofsignals received from said magnetic resonance imaging scanner toautomatically perform segmentation for said soft tissue region of saidsubject under observation by utilizing said at least one template andsaid at least one integrating feature, wherein performing saidsegmentation further comprises: transferring segmentation data from theat least one template to the at least one magnetic resonance image usinga non-linear and topology-preserved transformation; and performingsegmentation using the transferred segmentation data.
 2. The magneticresonance imaging system according to claim 1, wherein said at least oneintegrating feature provided by said at least one magnetic resonanceimage facilitates discerning said soft tissue region by at least one of:a correction of intensity homogeneity; a plurality of contrasts; asuppression of at least one tissue component; and a quantification of atleast one magnetic resonance (MR) contrast parameter, or variationsthereof.
 3. The magnetic resonance imaging system according to claim 1,wherein said signal processing system is at least partially incorporatedwithin a structure housing said magnetic resonance scanner.
 4. Themagnetic resonance imaging system according to claim 1, wherein saidsignal processing system is at least partially incorporated in aworkstation that is structurally separate and in communication with saidmagnetic resonance imaging scanner.
 5. The magnetic resonance imagingsystem according to claim 1, wherein said at least one template isobtained from magnetic resonance imaging.
 6. The magnetic resonanceimaging system according to claim 1, wherein said at least one templateincorporates at least one magnetic resonance image from at least onesubject that is different from said subject under observation.
 7. Themagnetic resonance imaging system according to claim 1, wherein said atleast one template incorporates at least one magnetic resonance imagefrom a previous scan of said subject under observation.
 8. Aworkstation, comprising: a preprocessing engine to receive andpreprocess magnetic resonance signals, from a magnetic resonancescanner, wherein said magnetic resonance signals form at least onemagnetic resonance image showing a soft tissue region from at least onesubject under observation in said magnetic resonance scanner; and anormalizing engine to automatically segment said soft tissue region bynormalizing the formed at least one magnetic resonance image based on atleast one template corresponding to said soft tissue region, whereinsegmentation data is transferred from the at least one template to theformed at least one magnetic resonance image using a non-linear andtopology-preserved transformation, wherein and the segmenting isperformed using the transferred segmentation data, and wherein said atleast one magnetic resonance image provides at least one integratingfeature to facilitate automatic segmentation.
 9. The workstationaccording to claim 8, wherein said at least one integrating featureprovided by said at least one magnetic resonance image facilitatesautomatic segmentation of said soft tissue region by at least one of: acorrection of intensity homogeneity; a plurality of contrasts; asuppression of at least one tissue component; and a quantification of atleast one magnetic resonance (MR) parameter, or variations thereof. 10.The workstation according to claim 8, wherein said at least one templateand the at least one magnetic resonance image being normalized are basedon at least one common magnetic resonance (MR) contrast.
 11. (canceled)12. The workstation according to claim 8, further comprising: ananalyzing engine to perform quantitative analysis associated with thesegmented soft tissue region.
 13. The workstation according to claim 12,wherein said analysis is related to at least one transformation matrixused by said normalizing engine.
 14. The workstation according to claim12, further comprising: a diagnosing engine constructed to compareresults from said quantitative analysis with a knowledge database. 15.The workstation according to claim 14, wherein said diagnosing engine isconstructed to compare by correlating statistics of said quantitativeanalysis and said knowledge database.
 16. The workstation according toclaim 14, wherein said knowledge database comprises data from a normalpopulation and a clinical population.
 17. A method of automaticsegmentation, comprising: receiving magnetic resonance signals forforming at least one magnetic resonance image showing a soft tissueregion from a subject, wherein said at least one magnetic resonanceimage provides at least one integrating feature to facilitate automaticsegmentation; preprocessing said magnetic resonance signals to generateat least one magnetic resonance image by utilizing said at least oneintegrating feature; receiving at least one template corresponding tosaid soft tissue region; normalizing the generated at least one magneticresonance image based on said at least one template to generate asegmented view of said soft tissue region, wherein said normalizingfurther comprises: transferring segmentation data from the at least onetemplate to the generated at least one magnetic resonance image using anon-linear and topology-preserved transformation; and generating thesegmented view using the transferred segmentation data.
 18. The methodaccording to claim 17, wherein said at least one integrating featureprovided by said at least one magnetic resonance image facilitatesautomatic segmentation by at least one of: a correction of intensityhomogeneity; a plurality of contrasts; a suppression of at least onetissue component; and a quantification of at least one magneticresonance (MR) parameter, and variations thereof.
 19. The methodaccording to claim 18, wherein said plurality of contrasts relates to atleast one of a relaxation time, a diffusion property, a perfusionproperty, a blood oxygenation level dependent property, a proton-densityproperty, and variations thereof.
 20. The method according to claim 17,wherein said at least one template and the at least one magneticresonance image are based on at least one common magnetic resonance (MR)contrast.
 21. The method according to claim 17, wherein said templatecontains information identifying said soft tissue region.
 22. The methodaccording to claim 21, wherein said information comprises at least oneprobabilistic map of said soft tissue region.
 23. (canceled)
 24. Themethod according to claim 17, further comprising: using at least oneexisting transformation matrix to normalize a new magnetic resonanceimage.
 25. The method according to claim 17, further comprising:analyzing data associated with the segmented soft tissue region.
 26. Themethod according to claim 25, wherein said data comprises at least onetransformation matrix used during said normalizing.
 27. The methodaccording to claim 25, further comprising: diagnosing by comparingresults from said analyzing with a knowledge database.
 28. The methodaccording to claim 27, wherein said comparing comprises correlatingbased on statistics associated with said analyzing and said knowledgedatabase.
 29. A computer system for processing automatically segmentedmagnetic resonance image data, the computer system comprising: apreprocessing engine to receive and preprocess magnetic resonancesignals, from a magnetic resonance scanner, wherein said magneticresonance signals form at least one magnetic resonance image showing asoft tissue region from at least one subject under observation in saidmagnetic resonance scanner; a normalizing engine to automaticallysegment said soft tissue region by normalizing the formed at least onemagnetic resonance image based on at least one template corresponding tosaid soft tissue region; a data storage unit comprising a knowledgedatabase that stores magnetic resonance image template data of aplurality of segmented brain data of various ages, sizes and stages ofpatients; and a diagnosing engine constructed to compare results fromsaid quantitative analysis associated with the segmented soft tissueregion with the knowledge database, wherein segmentation data istransferred from the at least one template to the formed at least onemagnetic resonance image using a non-linear and topology-preservedtransformation, wherein and the segmenting is performed using thetransferred segmentation data.
 30. The computer system according toclaim 29, wherein the results from said diagnosing are added to saidknowledge database when said diagnosing is positive.
 31. Acomputer-readable medium comprising software, which when executed by acomputer system, causes the computer system to: receive magneticresonance signals for forming at least one magnetic resonance imageshowing a soft tissue region from a subject, wherein said at least onemagnetic resonance image provides at least one integrating feature tofacilitate automatic segmentation; preprocess said magnetic resonancesignals to generate at least one magnetic resonance image by utilizingsaid at least one integrating feature; receive at least one templatecorresponding to said soft tissue region; and normalize the generated atleast one magnetic resonance image based on said at least one templateto generate a segmented view of said soft tissue region, wherein saidnormalizing further comprises: transferring segmentation data from theat least one template to the generated at least one magnetic resonanceimage using a non-linear and topology-preserved transformation; andgenerating the segmented view using the transferred segmentation data.32. The method according to claim 17, wherein said one magneticresonance imaging image is preprocessed to remove non brain tissues byusing templates that define brain boundaries.
 33. The method accordingto claim 22, wherein said at least one probabilistic map includesaverage values and standard deviations of normal and clinicalpopulations.
 34. The computer system according to claim 30, wherein thecomparison between the segmentation results of a patient and thesegmentation results in the knowledge database are performed bystatistical analysis, correlation analysis, or similarity searches.